Monte Carlo

Multigrid Monte Carlo Revisited: Theory and Bayesian Inference

Professor Robert Scheichl, University of Heidelberg

May 5, 11:00 - 12:00

B1 L3 R3119

Multigrid Monte Carlo

Abstract The fast simulation of Gaussian random fields plays a pivotal role in many research areas such as Uncertainty Quantification, data science and spatial statistics. In theory, it is a well understood and solved problem, but in practice the efficiency and performance of traditional sampling procedures degenerates quickly when the random field is discretised on a grid with spatial resolution going to zero. Most existing algorithms, such as Cholesky factorisation samplers, do not scale well on large-scale parallel computers. On the other hand, stationary, iterative approaches such as the